Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/141255| 題名: | COVID-19疫苗施打與網路輿情聲量關係: 以Moderna疫苗為例 How the internet opinion and sentiment influence the willingness of getting vaccinated? Case of the Moderna covid-19 vaccine |
作者: | 曾偉恩 TSENG, WEI-EN |
貢獻者: | 王信實 Wang, Shinn-Shyr 曾偉恩 TSENG, WEI-EN |
關鍵詞: | 網路輿情 聲量 疫苗施打 因果關係 合成控制法 Internet Pubilc Opinion Volume Vaccine Injection Causality Synthetic Control Method |
日期: | 2022 | 上傳時間: | 1-Aug-2022 | 摘要: | 近年來COVID-19大肆傳染，政府積極傳遞疫苗的相關資訊來防止疫情擴散，由於民眾接收COVID-19資訊不只是來自政府，還有一部分來自網路輿情，而網路上存在許多真假難辨的資訊，造就民眾產生施打疫苗的疑慮，使疫苗施打量無法達到政府預期，因此若政府釐清網路資訊與施打量的因果關係，或許能提高疫苗施打量。在此透過合成控制法 (Synthetic Control Method)，使用OpView資料庫的聲量資料，以及衛生福利部疾病管制署提供AstraZeneca、BioNTech、Moderna、Medigen四種疫苗在施打量，發現網路輿情與施打量之間存在相關性後，並嘗試找出其因果關係。 During the COVID-19 pandemic, the governments around the world actively disseminated the vaccine information and promoted the vaccination to prevent the epidemic. The mis- and dis-information about the vaccination on the internet usually makes people worried and thus decreases the willingness of vaccination. By using the Synthetic Control Method and the OpView data, as well as the AstraZeneca, BioNTech, Moderna, and Medigen vaccines provided by Taiwan Centers for Disease Control, this study investigates the negative causal relationship between the internet public opinion and vaccination. It is helpful to increase the number of people vaccinated by clarifying the causal relationship between internet information and vaccination. |
參考文獻: | 一、中文文獻：\n陳宜廷 (2019),“臺灣與南韓之經濟成長比較-合成控制法下的反事實分析”, 臺灣經濟預測與政策(中央研究院經濟研究所), 50(1), 1-410.\n\n二、英文文獻：\nAbadie, A. & J. Gardeazabal (2003), “The Economic Costs of Conflict: A Case Study of the Basque Country”, The American Economic Review, 93, 112–132.\nAbadie, A., A. Diamond & J. Hainmueller (2010), “Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program”, Journal of the American Statistical Association, 105:490, 493–505.\nAbadie, A., A. Diamond & J. Hainmueller (2015), “Comparative Politics and the Synthetic Control Method”, American Journal of Political Science, 59, 495–510.\nAbadie, A. & J. L’Hour (2021), “A Penalized Synthetic Control Estimator for Disaggregated Data”, Journal of the American Statistical Association, 116:536, 1817-1834.\nAbadie, A. (2021), “Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects”, Journal of Economic Literature, 59(2), 391-425.\nBen-Michael, E., A. Feller & J. Rothstein (2021), “The Augmented Synthetic Control Method”, Journal of the American Statistical Association, 116:536, 1789-1803.\nChen, Y.-T. (2020), “A distributional synthetic control method for policy evaluation”, Journal of Applied Econometrics, 35, 505-525.\nChen, Y.-T. (2022), “Regularization of Synthetic Controls for Policy Evaluation”, Department of Finance National Taiwan University.\nDoudchenko, N. & G. W. Imbens (2016), “Balancing, Regression, Difference-in-difference and synthetic control methods: A synthesis”, NBER Working Paper.\nFerman, B. & C. Pinto (2021), “Synthetic controls with imperfect pretreatment fit”, Quantitative Economics, 12, 1197-1221.\nFetzer, T., L. Hensel, J. Hermle & C. Roth (2020), “Coronavirus Perception and Economic Anxiety”, Review of Economics and Statistic, 2021, 103 (5), 968-978.\nLoomba, S. & A. D. Figueiredo et al. (2021), “Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA”, Nature Human Behaviour, 5, 337-348.\nMalmendier, U. & S. Nagel (2011),“Depression Babies: Do Macroeconomic Experi- ences Affect Risk Taking?”, The Quarterly Journal of Economics, 126 (1), 373-416.\nSaleska, J. & L. & Choi, K., R. (2021), “A behavioral economics. perspective on the COVID-19 vaccine amid public mistrust”, TBM, 11, 821-825.\nTversky, A. & D. Kahneman (1973), “Availability: A Heuristic for Judging Frequency and Probability”, Cognitive Psychology, 5 (2), 207-232.\nValero, R. (2015), “Synthetic Control Method versus Standard Statistical Techniques: a Comparison for Labor Market Reforms”, Working paper, University of Alincante.\nVergura, S. (2020), “Bollinger Bands Based on Exponential Moving Average for Statistical Monitoring of Multi-Array Photovoltaic Systems”, Energies, 13, 3992.\n\n三、中文書籍\n伊藤公一朗(王美娟譯) (2018)，《數據分析的力量》，台灣東販。\n\n四、英文書籍\nCunning, S. (2021), “Causal inference: The Mixtape”, Yale University.\nAngrist, J. D. & Pischke, J. S. (2009), “Most Harmless Econometrics: An Empiricist’s\nCompanion”, Princeton University. | 描述: | 碩士 國立政治大學 經濟學系 109258040 |
資料來源: | http://thesis.lib.nccu.edu.tw/record/#G0109258040 | 資料類型: | thesis |
| Appears in Collections: | 學位論文 |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 804001.pdf | 1.78 MB | Adobe PDF2 | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.